150 lines
5.9 KiB
R
150 lines
5.9 KiB
R
# Lab 10 for the University of Tulsa's CS-6643 Bioinformatics Course
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# Phylogenetic Analysis
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# Professor: Dr. McKinney, Fall 2022
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# Noah L. Schrick - 1492657
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## Set Working Directory to file directory - RStudio approach
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setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
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#### Part A: GenBank sequences and a multiple fasta file
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if (!require("ape")) install.packages("ape")
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library(ape) # needed for read.GenBank
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# fetch the mtDNA sequences
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mtDNA.MultiSeqs.list<-read.GenBank(c("AF011222","AF254446","X90314","AF089820",
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"AF176766","AF451972", "AY079510",
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"AF050738","AF176722","AF315498",
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"AF176731","AF451964"), as.character=TRUE)
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# look at species names
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mtDNA.Species<-attr(mtDNA.MultiSeqs.list,"species")
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# use species as name instead of genbank id
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names(mtDNA.MultiSeqs.list)<-mtDNA.Species
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# need to fix some names
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names(mtDNA.MultiSeqs.list)[1] <- paste("German_Neanderthal",sep="")
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names(mtDNA.MultiSeqs.list)[2] <- paste("Russian_Neanderthal",sep="")
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names(mtDNA.MultiSeqs.list)[3] <- paste("Human")
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names(mtDNA.MultiSeqs.list)[6] <- paste("Puti_Orangutan",sep="")
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names(mtDNA.MultiSeqs.list)[12] <- paste("Jari_Orangutan",sep="")
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length(mtDNA.MultiSeqs.list$Human)
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# look at one of the sequences using $
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mtDNA.MultiSeqs.list$Human
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## Convert to Biostrings object for the sequences
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if (!require("BiocManager")) install.packages("BiocManager")
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library(BiocManager)
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if (!require("Biostrings")) BiocManager::install("Biostrings")
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library(Biostrings)
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# loop through the list to create vector of strings for Biostrings input
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Names.vec <- c() # initialize speices names string vector
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Seqs.vec <- c() # initialize sequence string vector
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for (mtDNA.name in names(mtDNA.MultiSeqs.list))
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{
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Names.vec <- c(Names.vec,mtDNA.name) # concatenate vector
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Seqs.vec <-c(Seqs.vec,paste(mtDNA.MultiSeqs.list[[mtDNA.name]],collapse=""))
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}
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mtDNA.multSeqs.bstr <- DNAStringSet(Seqs.vec) # convert to Biostring
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# name the Biostring sequences and compute stats
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names(mtDNA.multSeqs.bstr) <- Names.vec # count nucs and sequence lengths
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num.nts <- alphabetFrequency(mtDNA.multSeqs.bstr)[,1:4]
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mtDNA.lengths <- rowSums(num.nts)
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proportion.nts <- num.nts/mtDNA.lengths
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# Obtain name and length of species with longest sequence
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nlengthnames <- cbind(mtDNA.lengths, Names.vec)
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idx <- which.max(nlengthnames[,1])
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nlengthnames[idx,]
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#### Part B: Multiple Sequence Alignment
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if (!require("BiocManager")) install.packages("BiocManager")
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library(BiocManager)
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if (!require("msa")) BiocManager::install("msa")
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library(msa)
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mtDNA.msa <- msa(mtDNA.multSeqs.bstr,method="ClustalOmega")
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msaPrettyPrint(mtDNA.msa, file="mtDNA.pdf", output="pdf", showNames="left",
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showLogo="none", askForOverwrite=FALSE, verbose=TRUE )
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## loop to make results data frame
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num_seqs <- length(Names.vec)
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# initialize data frame
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align.stats.df <- data.frame(species=rep(NA,num_seqs), seqlen=rep(0,num_seqs),
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numgaps=rep(0,num_seqs), nt_a=rep(NA,num_seqs),
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nt_c=rep(NA,num_seqs), nt_g=rep(NA,num_seqs),
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nt_t=rep(NA,num_seqs))
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# DNAbin type required for dist.dna and helpful for other calculations
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mtDNA.msa.DNAbin <- as.DNAbin(mtDNA.msa)
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for (i in 1:num_seqs){
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seq_name <- Names.vec[i]
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seq.vec <- as.character(mtDNA.msa.DNAbin[i,])
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num.gaps <- sum(seq.vec=="-")
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prop.nt.i <- proportion.nts[i,]
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align.stats.df[i,] <- c(seq_name, mtDNA.lengths[i], num.gaps,
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round(prop.nt.i[1],digits=2), round(prop.nt.i[2],digits=2),
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round(prop.nt.i[3],digits=2), round(prop.nt.i[4],digits=2))
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}
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# write to file
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write.table(align.stats.df,file="alignstats.tab",sep = "\t", row.names=FALSE, quote=FALSE)
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# you can use $ operator to grab a named column from a data.frame
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# similar to grabbing a named variable from a list
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align.stats.df$species
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align.stats.df$nt_a # strings by default
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as.numeric(align.stats.df$nt_a) # convert to numeric
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#### Part C: DNA distance matrices and phylogenetic trees
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# Compute Distances
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mtDNA.dist <- dist.dna(mtDNA.msa.DNAbin,model="K80")
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# manually find closest species
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mtDNA.dist.mat <-as.matrix(mtDNA.dist)
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diag(mtDNA.dist.mat)<-1 # force diagonal to be 1, not 0
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which(mtDNA.dist.mat == min(mtDNA.dist.mat), arr.ind = TRUE)
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## Make tree from distance matrix
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hc<- hclust(as.dist(mtDNA.dist.mat)) # transform to dist object first
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plot(hc,xlab="species",ylab="distance")
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## UPGMA
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if (!require("phangorn")) install.packages("phangorn")
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library(phangorn)
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mtDNA.tree.nj <- NJ(mtDNA.dist) # phangorn function
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plot(mtDNA.tree.nj, main="Neighbor Joining Tree (rooted) for primates")
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mtDNA.tree.upgma <- upgma(mtDNA.dist)
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plot(mtDNA.tree.upgma, show.node.label = TRUE, main="UPGMA Tree for Primates")
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source("msaUtils.R") # load msaConvert function into memory
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mtDNA.msa.phangorn <-msaConvert(mtDNA.msa,type="phangorn::phyDat")
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parsimony(mtDNA.tree.nj, mtDNA.msa.phangorn)
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# bootstrap to show support for tree edges
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# creates trees from bootstrap samples and checks how often
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# that edge appears. Show consistency of tree edge.
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bs.trees <- bootstrap.phyDat(mtDNA.msa.phangorn, FUN=function(x)NJ(dist.dna(as.DNAbin(x),model="K80")), bs=100)
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plotBS(mtDNA.tree.nj, bs.trees, "phylogram", main="Neighbor Joining")
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parsimony(mtDNA.tree.upgma, mtDNA.msa.phangorn)
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bs.upgma.trees <- bootstrap.phyDat(mtDNA.msa.phangorn, FUN=function(x)upgma(dist.dna(as.DNAbin(x),model="K80")), bs=100)
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plotBS(mtDNA.tree.upgma, bs.upgma.trees, "phylogram", main="UPGMA")
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#### Part D: Multidimensional Scaling
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# 2d MDS viz
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locs<-cmdscale(as.dist(mtDNA.dist))
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x<-locs[,1]
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y<-locs[,2]
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plot(x,y,main="Multi-dimensional Scaling",xlab="MDS dimension-1",ylab="MDS dimension-2", xlim=c(-.3,.35))
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text(x,y,rownames(locs),cex=0.5)
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library(rgl)
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locs<-cmdscale(as.dist(mtDNA.dist),k=3)
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x<-locs[,1]
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y<-locs[,2]
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z<-locs[,3]
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plot3d(x,y,z)
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text3d(x=x,y=y,z=z,texts=rownames(locs),cex=1.5)
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play3d(spin3d(axis=c(0,1,1), rpm=3), duration=30)
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